from datetime import datetime
import re
from IPython.display import display
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
date_pattern = re.compile(r"\d{1,2}/\d{1,2}/\d{2}")
def reformat_dates(col_name: str) -> str:
try:
return date_pattern.sub(datetime.strptime(col_name, "%m/%d/%y").strftime("%d/%m/%Y"), col_name, count=1)
except ValueError:
return col_name
# this github repo contains timeseries data for all coronavirus cases: https://github.com/CSSEGISandData/COVID-19
confirmed_cases_url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv"
deaths_url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv"
renamed_columns_map = {
"Country/Region": "country",
"Province/State": "location",
"Lat": "latitude",
"Long": "longitude"
}
cols_to_drop = ["location", "latitude", "longitude"]
confirmed_cases_df = (
pd.read_csv(confirmed_cases_url)
.rename(columns=renamed_columns_map)
.rename(columns=reformat_dates)
.drop(columns=cols_to_drop)
)
deaths_df = (
pd.read_csv(deaths_url)
.rename(columns=renamed_columns_map)
.rename(columns=reformat_dates)
.drop(columns=cols_to_drop)
)
display(confirmed_cases_df.head())
display(deaths_df.head())
# extract out just the relevant geographical data and join it to another .csv which has the country codes.
# The country codes are required for the plotting function to identify countries on the map
geo_data_df = confirmed_cases_df[["country"]].drop_duplicates()
country_codes_df = pd.read_csv("country_code_mapping.csv", usecols=["country", "alpha-3_code"], index_col="country")
geo_data_df = geo_data_df.join(country_codes_df, how="left", on="country").set_index("country")
dates_list = (
deaths_df.filter(regex=r"(\d{2}/\d{2}/\d{4})", axis=1)
.columns
.to_list()
)
# create a mapping of date -> dataframe, where each df holds the daily counts of cases and deaths per country
cases_by_date = {}
for date in dates_list:
confirmed_cases_day_df = confirmed_cases_df.filter(like=date, axis=1).rename(columns=lambda col: "confirmed_cases")
deaths_day_df = deaths_df.filter(like=date, axis=1).rename(columns=lambda col: "deaths")
cases_df = confirmed_cases_day_df.join(deaths_day_df).set_index(confirmed_cases_df["country"])
date_df = (
geo_data_df.join(cases_df)
.groupby("country")
.agg({"confirmed_cases": "sum", "deaths": "sum", "alpha-3_code": "first"})
)
date_df = date_df[date_df["confirmed_cases"] > 0].reset_index()
cases_by_date[date] = date_df
# each dataframe looks something like this:
cases_by_date[dates_list[-1]].head()
# helper function for when we produce the frames for the map animation
def frame_args(duration):
return {
"frame": {"duration": duration},
"mode": "immediate",
"fromcurrent": True,
"transition": {"duration": duration, "easing": "linear"},
}
fig = make_subplots(rows=2, cols=1, specs=[[{"type": "scattergeo"}], [{"type": "xy"}]], row_heights=[0.8, 0.2])
# set up the geo data, the slider, the play and pause buttons, and the title
fig.layout.geo = {"showcountries": True}
fig.layout.sliders = [{"active": 0, "steps": []}]
fig.layout.updatemenus = [
{
"type": "buttons",
"buttons": [
{
"label": "▶", # play symbol
"method": "animate",
"args": [None, frame_args(250)],
},
{
"label": "◼",
"method": "animate", # stop symbol
"args": [[None], frame_args(0)],
},
],
"showactive": False,
"direction": "left",
}
]
fig.layout.title = {"text": "Novel Coronavirus Case Tracker", "x": 0.5}
frames = []
steps = []
# set up colourbar tick values, ranging from 1 to the highest num. of confirmed cases for any country thus far
max_country_confirmed_cases = cases_by_date[dates_list[-1]]["confirmed_cases"].max()
# to account for the significant variance in number of cases, we want the scale to be logarithmic...
high_tick = np.log1p(max_country_confirmed_cases)
low_tick = np.log1p(1)
log_tick_values = np.geomspace(low_tick, high_tick, num=6)
# ...however, we want the /labels/ on the scale to be the actual number of cases (i.e. not log(n_cases))
visual_tick_values = np.expm1(log_tick_values).astype(int)
visual_tick_values[
-1
] = max_country_confirmed_cases # otherwise max cbar value might be max - 1 due to a rounding error
visual_tick_values = [f"{val:,}" for val in visual_tick_values]
# generate line chart data
# list of tuples: [(confirmed_cases, deaths), ...]
cases_deaths_totals = [(df.filter(like="confirmed_cases").astype("uint32").agg("sum")[0],
df.filter(like="deaths").astype("uint32").agg("sum")[0])
for df in cases_by_date.values()]
confirmed_cases_totals = [daily_total[0] for daily_total in cases_deaths_totals]
deaths_totals =[daily_total[1] for daily_total in cases_deaths_totals]
# this loop generates the data for each frame
for i, (date, data) in enumerate(cases_by_date.items(), start=1):
# to standardise the range and labels on the colourbar, we need to sneak in the min
# and max number of confirmed_cases
df = data
# the z-scale (for calculating the colour for each country) needs to be logarithmic
df["confirmed_cases_log"] = np.log1p(df["confirmed_cases"])
df["text"] = (
date
+ "<br>"
+ df["country"]
+ "<br>Confirmed cases: "
+ df["confirmed_cases"].apply(lambda x: "{:,}".format(x))
+ "<br>Deaths: "
+ df["deaths"].apply(lambda x: "{:,}".format(x))
)
# create the choropleth chart
choro_trace = go.Choropleth(
**{
"locations": df["alpha-3_code"],
"z": df["confirmed_cases_log"],
"zmax": high_tick,
"zmin": low_tick,
"colorscale": "reds",
"colorbar": {
"ticks": "outside",
"ticktext": visual_tick_values,
"tickmode": "array",
"tickvals": log_tick_values,
"title": {"text": "<b>Confirmed Cases</b>"},
"len": 0.8,
"y": 1,
"yanchor": "top"
},
"hovertemplate": df["text"],
"name": "",
"showlegend": False
}
)
# create the confirmed cases trace
confirmed_cases_trace = go.Scatter(
x=dates_list,
y=confirmed_cases_totals[:i],
mode="markers" if i == 1 else "lines",
name="Total Confirmed Cases",
line={"color": "Red"},
hovertemplate="%{x}<br>Total confirmed cases: %{y:,}<extra></extra>"
)
# create the deaths trace
deaths_trace = go.Scatter(
x=dates_list,
y=deaths_totals[:i],
mode="markers" if i == 1 else "lines",
name="Total Deaths",
line={"color": "Black"},
hovertemplate="%{x}<br>Total deaths: %{y:,}<extra></extra>"
)
if i == 1:
# the first frame is what the figure initially shows...
fig.add_trace(choro_trace, row=1, col=1)
fig.add_traces([confirmed_cases_trace, deaths_trace], rows=[2, 2], cols=[1, 1])
# ...and all the other frames are appended to the `frames` list and slider
frames.append(dict(data=[choro_trace, confirmed_cases_trace, deaths_trace], name=date))
steps.append(
{"args": [[date], frame_args(0)], "label": date, "method": "animate",}
)
fig.update_xaxes(range=[0, len(dates_list)-1], visible=False)
fig.update_yaxes(range=[0, max(confirmed_cases_totals)])
fig.frames = frames
fig.layout.sliders[0].steps = steps
fig.layout.geo.domain = {"x": [0,1], "y": [0.2, 1]}
fig.update_layout(height=650, legend={"x": 0.05, "y": 0.175, "yanchor": "top", "bgcolor": "rgba(0, 0, 0, 0)"})
fig
renamed_columns_map = {
"Country/Region": "country",
"Province/State": "location",
"Lat": "latitude",
"Long": "longitude"
}
confirmed_cases_df = (
pd.read_csv(confirmed_cases_url)
.rename(columns=renamed_columns_map)
.rename(columns=reformat_dates)
.fillna(method="bfill", axis=1)
)
deaths_df = (
pd.read_csv(deaths_url)
.rename(columns=renamed_columns_map)
.rename(columns=reformat_dates)
.fillna(method="bfill", axis=1)
)
display(confirmed_cases_df.head())
display(deaths_df.head())
fig = go.Figure()
geo_data_cols = ["country", "location", "latitude", "longitude"]
geo_data_df = confirmed_cases_df[geo_data_cols]
dates_list = (
confirmed_cases_df.filter(regex=r"(\d{2}/\d{2}/\d{4})", axis=1)
.columns
.to_list()
)
# create a mapping of date -> dataframe, where each df holds the daily counts of cases and deaths per country
cases_by_date = {}
for date in dates_list:
# get a pd.Series of all cases for the current day
confirmed_cases_day_df = (
confirmed_cases_df.filter(like=date, axis=1)
.rename(columns=lambda col: "confirmed_cases")
.astype("uint32")
)
# get a pd.Series of all deaths for the current day
deaths_day_df = (
deaths_df.filter(like=date, axis=1)
.rename(columns=lambda col: "deaths")
.astype("uint32")
)
cases_df = confirmed_cases_day_df.join(deaths_day_df) # combine the cases and deaths dfs
cases_df = geo_data_df.join(cases_df) # add in the geographical data
cases_df = cases_df[cases_df["confirmed_cases"] > 0] # get rid of any rows where there were no cases
cases_by_date[date] = cases_df
# each dataframe looks something like this:
cases_by_date[dates_list[-1]].head()
# generate the data for each day
fig.data = []
for date, df in cases_by_date.items():
df["confirmed_cases_norm"] = np.log1p(df["confirmed_cases"])
df["text"] = (
date
+ "<br>"
+ df["country"]
+ "<br>"
+ df["location"]
+ "<br>Confirmed cases: "
+ df["confirmed_cases"].astype(str)
+ "<br>Deaths: "
+ df["deaths"].astype(str)
)
fig.add_trace(
go.Scattergeo(
name="",
lat=df["latitude"],
lon=df["longitude"],
visible=False,
hovertemplate=df["text"],
showlegend=False,
marker={
"size": df["confirmed_cases_norm"] * 100,
"color": "red",
"opacity": 0.75,
"sizemode": "area",
},
)
)
# sort out the nitty gritty of the annotations and slider steps
annotation_text_template = "<b>Worldwide Totals</b>" \
"<br>{date}<br><br>" \
"Confirmed cases: {confirmed_cases:,d}<br>" \
"Deaths: {deaths:,d}<br>" \
"Mortality rate: {mortality_rate:.1%}"
annotation_dict = {
"x": 0.03,
"y": 0.35,
"width": 150,
"height": 110,
"showarrow": False,
"text": "",
"valign": "middle",
"visible": False,
"bordercolor": "black",
}
steps = []
for i, data in enumerate(fig.data):
step = {
"method": "update",
"args": [
{"visible": [False] * len(fig.data)},
{"annotations": [dict(annotation_dict) for _ in range(len(fig.data))]},
],
"label": dates_list[i],
}
# toggle the i'th trace and annotation box to visible
step["args"][0]["visible"][i] = True
step["args"][1]["annotations"][i]["visible"] = True
df = cases_by_date[dates_list[i]]
confirmed_cases = df["confirmed_cases"].sum()
deaths = df["deaths"].sum()
mortality_rate = deaths / confirmed_cases
step["args"][1]["annotations"][i]["text"] = annotation_text_template.format(
date=dates_list[i],
confirmed_cases=confirmed_cases,
deaths=deaths,
mortality_rate=mortality_rate,
)
steps.append(step)
sliders = [
{
"active": 0,
"currentvalue": {"prefix": "Date: "},
"steps": steps,
"len": 0.9,
"x": 0.05,
}
]
first_annotation_dict = {**annotation_dict}
first_annotation_dict.update(
{
"visible": True,
"text": annotation_text_template.format(
date="10/01/2020", confirmed_cases=44, deaths=1, mortality_rate=0.0227
),
}
)
fig.layout.title = {"text": "Novel Coronavirus Case Tracker", "x": 0.5}
fig.update_layout(
height=650,
margin={"t": 50, "b": 20, "l": 20, "r": 20},
annotations=[go.layout.Annotation(**first_annotation_dict)],
sliders=sliders,
)
fig.data[0].visible = True # set the first data point visible
fig
# save the figure locally as an interactive HTML page
fig.update_layout(height=1000)
fig.write_html("nCoV_tracker.html")